REFERENCES
1. Liu, Z. R.; Yao, B. N.; Zhang, R. F. SPaMD studio: an integrated platform for atomistic modeling, simulation, analysis, and visualization. Comput. Mater. Sci. 2022, 210, 111027.
2. Montoya, J. H.; Winther, K. T.; Flores, R. A.; Bligaard, T.; Hummelshøj, J. S.; Aykol, M. Autonomous intelligent agents for accelerated materials discovery. Chem. Sci. 2020, 11, 8517-32.
3. Wang, Z.; Huang, H.; Zhao, H.; et al. DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation. arXiv 2025, arXiv:2507.14267. Available online: https://doi.org/10.48550/arXiv.2507.14267 (accessed 8 October 2026).
4. Zou, Y.; Cheng, A. H.; Aldossary, A.; et al. El Agente: an autonomous agent for quantum chemistry. Matter 2025, 8, 102263.
5. Gustin, I.; Mantilla Calderón, L.; Pérez-Sánchez, J. B.; et al. El agente cuántico: automating quantum simulations. Rep. Prog. Phys. 2026, 89, 077602.
6. Hari Kumar, S. G.; Zou, Y.; Wang, A.; et al. El Agente Sólido: A New Age(nt) for Solid State Simulations. arXiv 2026, arXiv:2602.17886. Available online: https://doi.org/10.48550/arXiv.2602.17886 (accessed 8 October 2026).
7. Chen, W.; Xu, H.; Cheng, D. From unstructured to structured: opportunities, risks and engineering practices for LLM-based data extraction in chemistry and materials. AI. Agent. 2026, 2, 12.
8. Song, T.; Luo, M.; Zhang, X.; et al. A multiagent-driven robotic AI chemist enabling autonomous chemical research on demand. J. Am. Chem. Soc. 2025, 147, 12534-45.
9. Liu, G.; Yang, S.; Zhong, Y. Masgent: an AI-assisted materials simulation agent. Digit. Discov. 2026, 5, 2151-71.
10. Lv, S.; Peng, L.; Jiao, S.; Yao, Y. Wu, W; Hu, W. ChatMat: a multi-agent chemist for autonomous material prediction and exploration. Digit. Discov. 2026, 5, 2886-98.
11. Bin Faheem, A.; Han, Z.; Wu, D.; Li, H. AI-driven big data frameworks for electrode-electrolyte interphases in batteries. Adv. Mater. 2026, 38, e21975.



